Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies.
Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies.
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整合多个基因组数据以预测外显子组测序研究中致病的非同义单核苷酸变异
DOI：
10.1371/journal.pgen.1004237
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发表时间：
2014-03
期刊：
影响因子：
4.5
通讯作者：
Jiang R
中科院分区：
文献类型：
--
作者：
Wu J;Li Y;Jiang R
外显子组测序已被广泛用于检测人类遗传性疾病的致病性非同义单核苷酸变异（SNV）。然而，传统的统计遗传学方法在分析外显子组测序数据时是无效的，这是由于诸如大量测序的变体、致病性罕见变体或从头突变的不可忽略部分的存在以及受影响和正常群体的有限大小等事实。事实上，外显子组测序的普遍应用已经吸引了一种有效的计算方法，用于从大量测序的变体中鉴定致病性非同义SNV。在这里，我们提出了一种生物信息学方法，称为SPRING（通过整合基因组数据的SNV PRioritization），用于识别给定查询疾病的致病性非同义SNV。基于现有方法计算的6个功能效应评分（SIFT，PolyPhen 2，LRT，MutationTaster，GERP和PhyloP）和来自各种基因组数据源的五个关联评分（基因本体论、蛋白质-蛋白质相互作用、蛋白质序列、蛋白质结构域注释和基因通路注释），SPRING计算SNV是查询疾病的病因的统计显著性，因此提供了对候选SNV进行优先级排序的方法。通过一系列综合验证实验，我们证明了SPRING对于遗传基础部分已知或完全未知的疾病是有效的，并且对于具有多种遗传方式的疾病是有效的。在应用我们的方法对真实的外显子组测序数据集，我们显示了SPRING在检测自闭症，癫痫性脑病和智力残疾的致病性从头突变中的能力。我们还提供在线服务，独立软件和5，080种疾病致病SNV的全基因组预测，网址为http://bioinfo.au.tsinghua.edu.cn/spring。
Exome sequencing has been widely used in detecting pathogenic nonsynonymous single nucleotide variants (SNVs) for human inherited diseases. However, traditional statistical genetics methods are ineffective in analyzing exome sequencing data, due to such facts as the large number of sequenced variants, the presence of non-negligible fraction of pathogenic rare variants or de novo mutations, and the limited size of affected and normal populations. Indeed, prevalent applications of exome sequencing have been appealing for an effective computational method for identifying causative nonsynonymous SNVs from a large number of sequenced variants. Here, we propose a bioinformatics approach called SPRING (Snv PRioritization via the INtegration of Genomic data) for identifying pathogenic nonsynonymous SNVs for a given query disease. Based on six functional effect scores calculated by existing methods (SIFT, PolyPhen2, LRT, MutationTaster, GERP and PhyloP) and five association scores derived from a variety of genomic data sources (gene ontology, protein-protein interactions, protein sequences, protein domain annotations and gene pathway annotations), SPRING calculates the statistical significance that an SNV is causative for a query disease and hence provides a means of prioritizing candidate SNVs. With a series of comprehensive validation experiments, we demonstrate that SPRING is valid for diseases whose genetic bases are either partly known or completely unknown and effective for diseases with a variety of inheritance styles. In applications of our method to real exome sequencing data sets, we show the capability of SPRING in detecting causative de novo mutations for autism, epileptic encephalopathies and intellectual disability. We further provide an online service, the standalone software and genome-wide predictions of causative SNVs for 5,080 diseases at http://bioinfo.au.tsinghua.edu.cn/spring.
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影响因子：
16.2
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